1. Introduction and dataset

In this vignette, we will use the omnideconv package to deconvolve a bulk RNA-seq dataset from 24 breast cancer patients with two different methods (DWLS and BayesPrism). The datasets are from a recent breast cancer study (Wu et al. 2021). This study provides access to a primary-tumor single cell RNA-seq (scRNA-seq) dataset from 26 breast cancer patients across three major cancer subtypes (ER+, HER2+, TNBC). The dataset includes cell-type annotation for three resolution levels. In addition, the data includes bulk RNA-seq sequencing for 24 of the patients. In this chapter, we use multi-sample, breast-cancer scRNA-seq atlas (100,064 cells) as a reference to train the methods for the deconvolution of the bulk RNA-seq samples. The single-cell and bulk RNA-seq data is deposited on GEO, under accession number: GSE176078. The single cell data comes with the author’s cell type annotations.
We will need to download and unzip the datasets (GSE176078_Wu_etal_2021_BRCA_scRNASeq.tar.gz, GSE176078_Wu_etal_2021_bulkRNAseq_raw_counts.txt.gz), and store them in the working directory. For this example analysis, we will also need to retrieve the additional clinical information about the patients – although it is not required by omnideconv. This is available in the Supplementary Table 1, included with the paper supplementary materials

2. Library loading

library(Seurat)
## Loading required package: SeuratObject
## Loading required package: sp
## 
## Attaching package: 'SeuratObject'
## The following objects are masked from 'package:base':
## 
##     intersect, t
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(omnideconv)
## Loading required package: snowfall
## Loading required package: snow
## → checking omnideconv environment and dependencies
## + "C:/Users/c1041161/AppData/Local/miniconda3/condabin/conda.bat" install --yes --prefix "C:/Users/c1041161/AppData/Local/miniconda3/envs/r-omnideconv" -c conda-forge "anndata>=0.7.5"
library(readxl)
library(ggpubr)
## Loading required package: knitr

3. Single cell data processing

Although not strictly required by omnideconv, we suggest performing quality control and filtering of the input scRNA-seq data according to the best practice (Heumos et al. 2023), to ensure the best training conditions for deconvolution algorithms. We first pre-process the single-cell dataset to remove low-quality cells. We will use the R package Seurat (Hao et al. 2023), which allows fast and easy manipulation of single-cell data. We will create a Seurat object with the cell counts and their metadata of interest curated by the authors, which include cell-type annotation on three levels of resolution:

single.cell.data <- Seurat::ReadMtx(
  mtx = './Wu_etal_2021_BRCA_scRNASeq/count_matrix_sparse.mtx',
  cells = './Wu_etal_2021_BRCA_scRNASeq/count_matrix_barcodes.tsv',
  features = './Wu_etal_2021_BRCA_scRNASeq/count_matrix_genes.tsv',
  feature.column = 1
)
single.cell.metadata <- read.table('./Wu_etal_2021_BRCA_scRNASeq/metadata.csv', 
                                   sep = ',',
                                   header = TRUE, 
                                   row.names = 1)

single.cell.data = CreateSeuratObject(single.cell.data, 
                                      project='Wu_dataset', 
                                      assay='RNA', 
                                      min.cells = 0, 
                                      min.features = 1, meta.data = single.cell.metadata)

We can have an overview of the number of cells per cell type in the dataset:

table(single.cell.data$celltype_major, dnn = list("celltype_major")) %>%
      as.data.frame(., responseName = "number_cells")
##      celltype_major number_cells
## 1           B-cells         3206
## 2              CAFs         6573
## 3 Cancer Epithelial        24489
## 4       Endothelial         7605
## 5           Myeloid         9675
## 6 Normal Epithelial         4355
## 7      Plasmablasts         3524
## 8               PVL         5423
## 9           T-cells        35214

In order to remove low quality cells, we will follow the best practices for single cell normalization (Heumos et al. 2023). We will perform quality control on each cell by considering metrics such as the number of total counts, the number of expressed features (genes), and the fraction of mitochondrial genes per cell. The metric considered is the MAD, (Mean Absolute Deviation), computed as \(MAD = median(|X_i - median(X)|)\) with \(X_i\) being the respective metric of an observation (cell). We will consider as outliers cells that have \(X_i < n*|MAD|\), with \(n=5\) for number of counts and number of features, and n=3 for the fraction of mitochondrial genes.

is_outlier <- function(SeuratObject, metric, nmads){
  eval(parse(text = paste0("M <- SeuratObject$",metric)))
  outlier <- (M < median(M) - nmads * mad(M)) | (M > median(M) + nmads * mad(M))
  return(outlier)
}

check_outliers_nFeature <- is_outlier(single.cell.data, 'nFeature_RNA', 5)
check_outliers_nCount <- is_outlier(single.cell.data, 'nCount_RNA', 5)
check_outliers_mito <- is_outlier(single.cell.data, 'percent.mito', 3)

non_outliers_nFeature <- names(check_outliers_nFeature[!check_outliers_nFeature])
non_outliers_nCount <- names(check_outliers_nCount[!check_outliers_nCount])
non_outliers_mito <- names(check_outliers_mito[!check_outliers_mito])

We will retain only those that satisfy all three of the conditions described above.

non_outliers <- intersect(non_outliers_nFeature, non_outliers_nCount) %>% 
  intersect(non_outliers_mito)

single.cell.data <- subset(single.cell.data, cells = non_outliers)

as.data.frame(table(single.cell.data$celltype_major, dnn = list("celltype_major")), responseName = "number_cells")
##      celltype_major number_cells
## 1           B-cells         3150
## 2              CAFs         6154
## 3 Cancer Epithelial        16613
## 4       Endothelial         6991
## 5           Myeloid         8855
## 6 Normal Epithelial         3533
## 7      Plasmablasts         3164
## 8               PVL         5120
## 9           T-cells        34991

4. Bulk data preprocessing

We will now read in the bulk sequencing data file, which consists of 24 samples.

bulk.data <- read.table('C:/Users/c1041161/book_chapter/GSE176078_Wu_etal_2021_bulkRNAseq_raw_counts.txt', skip=1)

header <- read.table('C:/Users/c1041161/book_chapter/GSE176078_Wu_etal_2021_bulkRNAseq_raw_counts.txt', 
                     header = FALSE, nrows = 1, skipNul = TRUE, sep='\t')

colnames(bulk.data) <- c('Genes', gsub('A|N', '', header[2:25]))

bulk.data <- bulk.data[bulk.data$Genes != '', ]
bulk.data <- column_to_rownames(bulk.data, 'Genes')
bulk.data <- as.matrix(bulk.data)

5. Subsampling of single cell data

The various methods included in omnideconv rely on the single cell dataset that will be used to train them for the deconvolution of those specific cell types. This training involves the optimization of internal features of the methods and can happen in different ways. Some methods use the single cell data to build a ‘signature matrix’, i.e. a reduced transcriptional fingerprints of the cell types provided, while others use this data in a statistical or deep learning model. Since single cell datasets can often encompass thousands of cells, we will need to subsample it in order to be able to run the analysis on our machines. In this case we will retain a maximum of 200 cells per cell type, but this step can be costumed, or eventually skipped, depending on the computational resources available.

set.seed(42)

max_cells_per_celltype = 200

sampled.metadata <- single.cell.data@meta.data %>%
      rownames_to_column(., 'barcode') %>%
      group_by(., celltype_major) %>% 
      nest() %>%            
      mutate(n =  map_dbl(data, nrow)) %>%
      mutate(n = min(n, max_cells_per_celltype)) %>%
      ungroup() %>% 
      mutate(samp = map2(data, n, sample_n)) %>% 
      select(-data) %>%
      unnest(samp)

single.cell.data.sampled <- subset(single.cell.data, cells = sampled.metadata$barcode)


as.data.frame(table(single.cell.data.sampled$celltype_major, dnn = list("celltype_major")), responseName = "number_cells")
##      celltype_major number_cells
## 1           B-cells          200
## 2              CAFs          200
## 3 Cancer Epithelial          200
## 4       Endothelial          200
## 5           Myeloid          200
## 6 Normal Epithelial          200
## 7      Plasmablasts          200
## 8               PVL          200
## 9           T-cells          200

6. Deconvolution of the bulk data

Each methods has different requirements, but in general to compute the deconvolution results we will need the single cell counts matrix, the cell type annotations and the information on the individual/experiment fom which the cells were retrieved (batch ID).

counts.matrix <- as.matrix(single.cell.data.sampled@assays$RNA$counts)
cell.type.annotations <- single.cell.data.sampled$celltype_major
batch.ids <- single.cell.data.sampled$orig.ident

6.1 Deconvolution with DWLS

Now we’re going to deconvolute the bulk dataset with different methods. The first one we are going to use is called DWLS (Tsoucas et al. 2019) and performs the deconvolution in a two-steps process. First, the single cell data is used to build a signature matrix using the omnideconv function build_model. DWLS looks for differentially expressed genes that discriminate across cell types, and can do so with two approaches based either on the Seurat (Hao et al. 2023) “bimod” test (McDavid et al. 2012) or on MAST (Finak et al. 2015). MAST improves the former model, but has an increased computational requirement (Nault et al. 2022). The authors recommend using this method on the smaller datasets, and to switch to Seurat if the analysis with MAST cannot be completed. To reduce MAST’s computational time, we introduced a second version of the MAST-based function (mast_optimized) that speeds up the process compared to the original implementation:

# We need to insert the normalization as well
signature.matrix.dwls <- omnideconv::build_model(single_cell_object = counts.matrix,
                        cell_type_annotations = cell.type.annotations,
                        method = 'dwls', 
                        dwls_method = 'mast_optimized')

This signature is optimized so that the genes selected are the ones that help to discriminate across cell types.

head(signature.matrix.dwls)
##   Gene.ID Endothelial  CAFs   PVL B.cells T.cells Myeloid Normal.Epithelial
## 1   ACKR1      15.230 0.075 0.040   0.025   0.005   0.020             0.095
## 2   PLVAP      10.680 0.110 0.040   0.015   0.005   0.030             0.105
## 3     VWF       6.065 0.065 0.020   0.000   0.020   0.000             0.010
## 4   RAMP2       8.665 0.210 0.025   0.005   0.005   0.005             0.155
## 5   CLDN5       5.190 0.115 0.025   0.000   0.000   0.010             0.020
## 6    AQP1       6.450 0.160 0.125   0.015   0.000   0.015             0.095
##   Plasmablasts Cancer.Epithelial
## 1        0.010             0.000
## 2        0.005             0.030
## 3        0.005             0.015
## 4        0.020             0.195
## 5        0.000             0.010
## 6        0.000             0.030

The signature is now used for the deconvolution of the bulk RNAseq, which is performed with the omnideconv function deconvolute. DWLS computes the cell fractions performing one of Ordinary Least Squares (OLS) Regression, Support Vector Regression (SVR) or the Dampened Weighted Least Squares Regression (DWLS) that was introduced with the method. This last regression method is shown to outperform the others when it comes to the detection of rare cell types:

deconvolution.results.dwls <- deconvolute(bulk_gene_expression = bulk.data,
                                          signature = signature.matrix.dwls,
                                          method='dwls',
                                          dwls_submethod = 'DampenedWLS')

We will now obtain, for every sample, a set of cell type fractions for each cell type that was included in the provided single cell dataset.

head(deconvolution.results.dwls)
##              B-cells      CAFs Cancer Epithelial Endothelial     Myeloid
## CID3586 1.582901e-01 0.2518742        0.09114178  0.06271542 0.042637219
## CID3921 1.681912e-01 0.2021584        0.08099052  0.09070612 0.017635665
## CID3941 3.711408e-02 0.2214264        0.27247883  0.07200429 0.040131349
## CID3948 1.236697e-01 0.1220122        0.39791826  0.02523021 0.002531945
## CID3963 2.734761e-02 0.2066542        0.01207755  0.05969586 0.131974995
## CID4066 2.742824e-11 0.4308795        0.15812117  0.09552470 0.002181170
##         Normal Epithelial  Plasmablasts        PVL    T-cells
## CID3586      1.547317e-01 -2.344791e-33 0.03221636 0.20639327
## CID3921      1.011845e-01  2.417927e-04 0.04400799 0.29488375
## CID3941      2.792117e-01  2.767974e-22 0.06969082 0.00794252
## CID3948      2.199138e-16  1.743133e-03 0.05252235 0.27437215
## CID3963      3.416887e-01 -1.008382e-21 0.03098350 0.18957754
## CID4066      2.084751e-01 -7.112521e-21 0.03140592 0.07341250

We can also visualise the results as a barplot trough the built-in plot_deconvolution function

omnideconv::plot_deconvolution(list('dwls' = deconvolution.results.dwls), "bar", "method", "Spectral")

6.2 BayesPrism deconvolution

The third method we will use is BAyesPrism (Chu et al. 2022). This method is based on a Bayesian framework and models the transcriptomic expression observed in the scRNA-seq dataset. It then uses this information to dissect te bulk RNA-seq.

# BayesPrism deconvolution

deconvolution.results.bayesprism <- deconvolute(bulk_gene_expression = bulk.data,
                                           single_cell_object = counts.matrix,
                                           cell_type_annotations = cell.type.annotations,
                                           method = 'bayesprism', 
                                           n_cores=12)

We can visualize the results as before:

omnideconv::plot_deconvolution(list('bayesprism' = deconvolution.results.bayesprism), "bar", "method", "Spectral")

7. Deconvolution of cell types at a lower resolution

The considered single-cell breast cancer dataset includes cell-type annotations at three levels of resolution: celltype_major, celltype_minor, and celltype_subset, which distinguish 9, 29, and 58 cell types respectively. The different cell-type annotations can be accessed with:

single.cell.data$celltype_major   # Major annotation
single.cell.data$celltype_minor   # Minor annotation
single.cell.data$celltype_subset           # Subset annotation

These additional annotations provide a cell-type classification at a finer resolution. For instance, at the celltype_major level, we only have the T cell population, while at the celltype_minor level, we can distinguish between CD4+ and CD8+ T cells. In the following, we will again perform deconvolution analysis with DWLS but, this time, using the celltype_minor information. We will subsample the dataset as before, this time considering the second level of resolution for the cell types, and extract the objects needed for deconvolution.

set.seed(42)

max_cells_per_celltype = 200


sampled.metadata <- single.cell.data@meta.data %>%
      rownames_to_column(., 'barcode') %>%
      group_by(., celltype_minor) %>% 
      nest() %>%            
      mutate(n =  map_dbl(data, nrow)) %>%
      mutate(n = min(n, max_cells_per_celltype)) %>%
      ungroup() %>% 
      mutate(samp = map2(data, n, sample_n)) %>% 
      select(-data) %>%
      unnest(samp)

single.cell.data.sampled <- subset(single.cell.data, cells = sampled.metadata$barcode)


as.data.frame(table(single.cell.data.sampled$celltype_minor, dnn = list("celltype_minor")), responseName = "number_cells")
##                 celltype_minor number_cells
## 1               B cells Memory          200
## 2                B cells Naive          200
## 3           CAFs MSC iCAF-like          200
## 4              CAFs myCAF-like          200
## 5              Cancer Basal SC          200
## 6               Cancer Cycling          200
## 7               Cancer Her2 SC          200
## 8               Cancer LumA SC          200
## 9               Cancer LumB SC          200
## 10                 Cycling PVL           37
## 11             Cycling T-cells          200
## 12             Cycling_Myeloid          200
## 13                         DCs          200
## 14           Endothelial ACKR1          200
## 15          Endothelial CXCL12          200
## 16 Endothelial Lymphatic LYVE1          183
## 17            Endothelial RGS5          200
## 18         Luminal Progenitors          200
## 19                  Macrophage          200
## 20              Mature Luminal          200
## 21                    Monocyte          200
## 22               Myoepithelial          200
## 23                    NK cells          200
## 24                   NKT cells          200
## 25                Plasmablasts          200
## 26          PVL Differentiated          200
## 27                PVL Immature          200
## 28                T cells CD4+          200
## 29                T cells CD8+          200
counts.matrix <- as.matrix(single.cell.data.sampled@assays$RNA$counts)
cell.type.annotations <- single.cell.data.sampled$celltype_minor
batch.ids <- single.cell.data.sampled$orig.ident
signature.matrix.dwls.minor <- omnideconv::build_model(single_cell_object = counts.matrix,
                        cell_type_annotations = cell.type.annotations,
                        method = 'dwls', 
                        dwls_method = 'mast_optimized')

deconvolution.results.dwls.minor <- deconvolute(bulk_gene_expression = bulk.data,
                                          model = signature.matrix.dwls.minor,
                                          method='dwls',
                                          dwls_submethod = 'DampenedWLS')

We can visualize the results as before:

omnideconv::plot_deconvolution(list('dwls' = deconvolution.results.dwls.minor), "bar", "method", "Spectral")

8. Comparison of cell fractions across methods

dwls.results.long <- deconvolution.results.dwls %>%
     as.data.frame() %>%
     rownames_to_column(., 'Sample') %>%
     gather(., key = 'cell_type', value = 'dwls_fraction', -'Sample')

bayesprism.results.long <- deconvolution.results.bayesprism %>%
     as.data.frame() %>%
     rownames_to_column(., 'Sample') %>%
     gather(., key = 'cell_type', value = 'bayesprism_fraction', -'Sample')

results.long <- full_join(dwls.results.long, bayesprism.results.long)
## Joining with `by = join_by(Sample, cell_type)`
ggplot(results.long, aes(x = dwls_fraction, y = bayesprism_fraction)) +
  geom_abline(linetype = 'dashed') +
  geom_point(aes(color = cell_type)) +
  theme_bw()+
  theme(legend.position = 'hide') +
  facet_wrap(.~cell_type) +
  ylab('BayesPrism fractions')+
  xlab('DWLS fractions')

9. Comparison of cell fractions across conditions

We can consider as well the metadata provided with the original paper, which include patient’s data, treatment details, and more. We can first harmonize the sample names, and then combine all this information with the deconvolution results in one dataframe.

patient.metadata <- read_excel("C:/Users/c1041161/book_chapter/41588_2021_911_MOESM4_ESM.xlsx", sheet = 1, skip = 3) %>%
  select(., c(1, 4, 5, 12))
colnames(patient.metadata) <- c('Sample', 'Grade', 'Cancer_type', 'Treatment')

patient.metadata$Sample <- gsub('-', '', patient.metadata$Sample) %>%
  paste0('CID', .)

patients.results <- rownames_to_column(as.data.frame(deconvolution.results.dwls), 'Sample') %>%
  gather(., key='celltype', value='cell_fraction', -Sample) %>%
  left_join(., patient.metadata)
## Joining with `by = join_by(Sample)`
as.data.frame(table(patient.metadata$Treatment, dnn = list("Treatment")), responseName = "number_patients")
##   Treatment number_patients
## 1     Naïve              21
## 2   Treated               5

In this cohort, 5 out of 24 patients were treated with neoadjuvant and/or Paclitaxel therapy, while the remaining 19 were untreated. We can visualize the fractions deconvolved by DWLS for every cell type, stratifying the patients into treated and untreated ones

ggplot(patients.results, 
       aes(x = Treatment, y = cell_fraction, fill = Treatment)) +
  geom_boxplot() +
  facet_wrap(.~celltype, scales = 'free_y', ncol = 4) +
  ylab('cell fractions') +
  xlab('') +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(label='p.signif', method = "wilcox.test",
                     comparisons = list(c("Naïve", "Treated")), 
                     hide.ns = TRUE) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.25)))

10. Comparison of cell fractions across cancer subtype

By considering deconvolution results based on the fine-grained annotations, we can quantify additional cell types including different molecular subtypes of tumor cells: basal, Her2, and luminal A and B. The classification of malignant epithelial cells was performed at the single-cell level in the original publication using a new tool, SCSubtype (Wu et al. 2021). In parallel, the authors stratified the bulk RNA-seq samples into tumor molecular subtypes (luminal-like A/B, HER2-enriched, basal-like and normal-like) using the PAM50 gene signature (Pu et al. 2020):

patient.pam50.typing <- read_excel("C:/Users/c1041161/book_chapter/41588_2021_911_MOESM4_ESM.xlsx", 
                                   sheet = 3, skip = 3) %>%
                                   select(., c(1, 4))
colnames(patient.pam50.typing) <- c('Sample', 'PAM50_subtype')


as.data.frame(table(patient.pam50.typing$PAM50_subtype, dnn = list("PAM50_subtype")), 
              responseName = "number_patients")
##   PAM50_subtype number_patients
## 1         Basal               6
## 2          Her2               1
## 3          LumA               5
## 4          LumB               4
## 5        Normal               2
## 6 Not available               2
patients.results.minor <- rownames_to_column(as.data.frame(deconvolution.results.dwls.minor), 'Sample') %>%
  gather(., key='celltype', value='cell_fraction', -Sample) %>%
  left_join(., patient.pam50.typing)
## Joining with `by = join_by(Sample)`
patients.results.minor$PAM50_subtype[is.na(patients.results.minor$PAM50_subtype)] <- 'Unkn.'

We can then visualize the distribution of the fractions estimated by DWLS for the different cancer-cell molecular subtypes, stratifying the samples according to the PAM50 classifications:

patients.results.minor %>%
 filter(celltype %in% c('Cancer Basal SC', 'Cancer LumA SC', 
             'Cancer LumB SC', 'Cancer Her2 SC')) %>%
ggplot(., aes(x = PAM50_subtype, y = cell_fraction, fill = PAM50_subtype)) +
  geom_boxplot() +
  facet_wrap(.~celltype, scales = 'free_y', ncol = 2) +
  ylab('cell fractions') +
  xlab('') +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), 
        legend.position = 'None') +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.25)))

We can see that DWLS identifies the correct cancer-cell molecular subtype as the most abundant in corresponding samples, although with some discrepancies, especially for luminal A cells found in normal samples and luminal B cells found in luminal A samples.

References

Chu, Tinyi, Zhong Wang, Dana Pe’er, and Charles G. Danko. 2022. “Cell Type and Gene Expression Deconvolution with BayesPrism Enables Bayesian Integrative Analysis Across Bulk and Single-Cell RNA Sequencing in Oncology.” Nature Cancer 3 (4): 505–17. https://doi.org/10.1038/s43018-022-00356-3.
Finak, Greg, Andrew McDavid, Masanao Yajima, Jingyuan Deng, Vivian Gersuk, Alex K. Shalek, Chloe K. Slichter, et al. 2015. “MAST: A Flexible Statistical Framework for Assessing Transcriptional Changes and Characterizing Heterogeneity in Single-Cell RNA Sequencing Data.” Genome Biology 16 (1). https://doi.org/10.1186/s13059-015-0844-5.
Hao, Yuhan, Tim Stuart, Madeline H. Kowalski, Saket Choudhary, Paul Hoffman, Austin Hartman, Avi Srivastava, et al. 2023. “Dictionary Learning for Integrative, Multimodal and Scalable Single-Cell Analysis.” Nature Biotechnology 42 (2): 293–304. https://doi.org/10.1038/s41587-023-01767-y.
Heumos, Lukas, Anna C. Schaar, Christopher Lance, Anastasia Litinetskaya, Felix Drost, Luke Zappia, Malte D. Lücken, et al. 2023. “Best Practices for Single-Cell Analysis Across Modalities.” Nature Reviews Genetics 24 (8): 550–72. https://doi.org/10.1038/s41576-023-00586-w.
McDavid, Andrew, Greg Finak, Pratip K. Chattopadyay, Maria Dominguez, Laurie Lamoreaux, Steven S. Ma, Mario Roederer, and Raphael Gottardo. 2012. “Data Exploration, Quality Control and Testing in Single-Cell qPCR-Based Gene Expression Experiments.” Bioinformatics 29 (4): 461–67. https://doi.org/10.1093/bioinformatics/bts714.
Nault, Rance, Satabdi Saha, Sudin Bhattacharya, Jack Dodson, Samiran Sinha, Tapabrata Maiti, and Tim Zacharewski. 2022. “Benchmarking of a Bayesian Single Cell RNAseq Differential Gene Expression Test for Dose–Response Study Designs.” Nucleic Acids Research 50 (8): e48–48. https://doi.org/10.1093/nar/gkac019.
Pu, Minya, Karen Messer, Sherri R Davies, Tammi L Vickery, Emily Pittman, Barbara A Parker, Matthew J Ellis, et al. 2020. “Research-Based PAM50 Signature and Long-Term Breast Cancer Survival.” Breast Cancer Res. Treat. 179 (1): 197–206.
Tsoucas, Daphne, Rui Dong, Haide Chen, Qian Zhu, Guoji Guo, and Guo-Cheng Yuan. 2019. “Accurate Estimation of Cell-Type Composition from Gene Expression Data.” Nature Communications 10 (1). https://doi.org/10.1038/s41467-019-10802-z.
Wu, Sunny Z., Ghamdan Al-Eryani, Daniel Lee Roden, Simon Junankar, Kate Harvey, Alma Andersson, Aatish Thennavan, et al. 2021. “A Single-Cell and Spatially Resolved Atlas of Human Breast Cancers.” Nature Genetics 53 (9): 1334–47. https://doi.org/10.1038/s41588-021-00911-1.